• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.2
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• pp.328-333
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• 2003

This paper did to do with 18 muscle pairs that do fetters in anatomy that influence in facial expression change and mix motion of muscle for face facial animation. After set and change mash and make standard model in individual's image, did mapping to mash using individual facial front side and side image to raise truth stuff. Muscle model who become motive power that can do animation used facial expression creation correcting Waters' muscle model. Created deformed face that texture is dressed using these method. Also, 6 facial expression that Ekman proposes did animation.

• The Journal of Korean Medicine
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• v.29 no.3
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• pp.131-143
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• 2008

Objectives: Electroacupuncture has the effect of recovering paralytic nerves and muscles. To treat disproportional muscles of expression with electroacupuncture, it is essential that we know the correct point of paralytic muscle. Methods: We investigated 20 cases of patients with facial palsy sequelae. We measured nasolabial angles, checked grade of muscle palsy, and tested ENoG. Results: This study showed significant correlation between nasolabial angles with these muscle groups (zygomatic group I, zygomatic group II, orbicularis oris muscle). Conclusions: Disproportional facesare fixed by muscles of expression observed in facial palsy sequelae. We can treat muscular paralysis of these muscle groups with electroacupuncture for more complete recovery.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.932-938
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• 2012

Facial expression has its significance in mutual communication. It is the only means to express human's countless inner feelings better than the diverse languages human use. This paper suggests muscle model-based 3D facial expression generation system to produce easy and natural facial expressions. Based on Waters' muscle model, it adds and used necessary muscles to produce natural facial expressions. Also, among the complex elements to produce expressions, it focuses on core, feature elements of a face such as eyebrows, eyes, nose, mouth, and cheeks and uses facial muscles and muscle vectors to do the grouping of facial muscles connected anatomically. By simplifying and reconstructing AU, the basic nuit of facial expression changes, it generates easy and natural facial expressions.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.477-486
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• 2008

Facial expression, which changes face geometry, usually has an adverse effect on the performance of a face recognition system. To improve the face recognition rate, we propose a normalization method of facial expression to diminish the difference of facial expression between probe and gallery faces. Two approaches are used to facial expression modeling and normalization from single still images using a generic facial muscle model without the need of large image databases. The first approach estimates the geometry parameters of linear muscle models to obtain a biologically inspired model of the facial expression which may be changed intuitively afterwards. The second approach uses RBF(Radial Basis Function) based interpolation and warping to normalize the facial muscle model as unexpressed face according to the given expression. As a preprocessing stage for face recognition, these approach could achieve significantly higher recognition rates than in the un-normalized case based on the eigenface approach, local binary patterns and a grey-scale correlation measure.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1187-1193
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• 2013

In this paper, we present realistic 3D head modeling and facial expression systems. For 3D head modeling, we perform generic model fitting to make individual head shape and texture mapping. To calculate the deformation function in the generic model fitting, we determine correspondence between individual heads and the generic model. Then, we reconstruct the feature points to 3D with simultaneously captured images from calibrated stereo camera. For texture mapping, we project the fitted generic model to image and map the texture in the predefined triangle mesh to generic model. To prevent extracting the wrong texture, we propose a simple method using a modified interpolation function. For generating 3D facial expression, we use the vector muscle based algorithm. For more realistic facial expression, we add the deformation of the skin according to the jaw rotation to basic vector muscle model and apply mass spring model. Finally, several 3D facial expression results are shown at the end of the paper.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1029-1032
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• 1999

Muscle based face image synthesis is one of the most realistic approach to realize life-like agent in computer. Facial muscle model is composed of facial tissue elements and muscles. In this model, forces are calculated effecting facial tissue element by contraction of each muscle strength, so the combination of each muscle parameter decide a specific facial expression. Now each muscle parameter is decided on trial and error procedure comparing the sample photograph and generated image using our Muscle-Editor to generate a specific face image. In this paper, we propose the strategy of automatic estimation of facial muscle parameters from 2D marker movement using neural network. This also 3D motion estimation from 2D point or flow information in captered image under restriction of physics based face model.

• Journal of Oral Medicine and Pain
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• v.34 no.3
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• pp.317-324
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• 2009

This study aimed to assess stiffness and elasticity of the masticatory muscle in the patients with the masticatory muscle pain using a tactile sensor and to investigate whether the masticatory muscle pain affects the facial expression muscles. From those who visited Department of Oral Medicine in Dankook University Dental Hospital, 27 patients presenting with unilateral muscle pain and tenderness in the masseter muscle (Ms) were selected (mean age: $36.4{\pm}13.8$ years). Exclusion criterion was those who also had temporomandibular joint (TMJ) disorders or any neurological pain. Muscle stiffness and elasticity for the muscles of mastication and facial expression was investigated with the tactile sensor (Venustron, Axiom Co., JAPAN) and the muscles measured were the Ms, anterior temporal muscle (Ta), frontalis (Fr), inferior orbicularis oculi (Ooci), zygomaticus major (Zm), superior and inferior orbicularis oris (Oors, Oori) and mentalis (Mn). t-tests was used to compare side difference in muscle stiffness and elasticity. Side differences were also compared between diagnostic groups (local muscle soreness (LMS) vs myofascial pain syndrome (MPS) and between acute (< 6M) and chronic ($\geq$ 6M) groups. This study showed that Ms and Zm at affected side exhibited significantly increased stiffness and decreased elasticity as compared to the unaffected side.(p<0.05) There was no significant difference between local muscle soreness and myofascial pain syndrome groups and between acute and chronic groups. The results of this study suggests that masticatory muscle pain in Ms can affect muscle stiffness and elasticity not only for Ms but also for Zm, the facial expression muscle.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2012.05a
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• pp.75-76
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• 2012

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.33-38
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• 1999

Muscle based face image synthesis is one of the most realistic approach to realize life-like agent in computer. Facial muscle model is composed of facial tissue elements and muscles. In this model, forces are calculated effecting facial tissue element by contraction of each muscle strength, so the combination of each muscle parameter decide a specific facial expression. Now each muscle parameter is decided on trial and error procedure comparing the sample photograph and generated image using our Muscle-Editor to generate a specific race image. In this paper, we propose the strategy of automatic estimation of facial muscle parameters from 2D marker movement using neural network. This also 3D motion estimation from 2D point or flow information in captered image under restriction of physics based fare model.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.162-169
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• 2019

Facial rigging technology has been developing more and more since the 21st century. Facial rigging of various methods is still attempted and a technique of capturing the geometry in real time recently also appears. Currently Modern CG is produced image which is hard to distinguish from actual photograph. However, this kind of technology still requires a lot of equipment and cost. The purpose of this study is to perform facial rigging using muscle simulation instead of using such equipment. Original muscle simulations were made primarily for use in the body of a creature. In this study, however, we use muscle simulations for facial rigging to create a more realistic creature-like effect. To do this, we used Ziva Dynamics' Ziva VFX muscle simulation software. We also develop a method to overcome the disadvantages of muscle simulation. Muscle simulation can not be applied in real time and it takes time to simulate. It also takes a long time to work because the complex muscles must be connected. Our study have solved this problem using blendshape and we want to show you how to apply our method to face rig.